Understanding the Problem

High cardinality metrics in Prometheus, combined with inefficient PromQL queries, can cause significant performance issues, including high memory usage and slow query execution. These issues impact dashboards, alerts, and overall system reliability.

Root Causes

1. High Cardinality Metrics

Metrics with excessive label combinations (high cardinality) generate a large number of time series, overwhelming Prometheus' storage and query engine.

2. Inefficient PromQL Queries

PromQL queries that use regex matching or unoptimized operators can drastically increase query execution time.

3. Lack of Query Caching

Frequent execution of identical queries without caching increases Prometheus server load.

4. Overloaded Prometheus Instances

Running too many scrape targets or retaining metrics for extended periods can overwhelm Prometheus' storage and query subsystems.

Diagnosing the Problem

Prometheus provides tools to monitor and diagnose query performance issues. Use /metrics endpoint to track query durations:

prometheus_http_request_duration_seconds_bucket{handler="/api/v1/query_range"}

Enable query logging by setting --log.level=debug to analyze query patterns.

In Grafana, use the Prometheus Query Inspector to examine the execution time and response size of specific queries.

Solutions

1. Reduce Metric Cardinality

Identify high cardinality metrics using:

count(count by (__name__, label_name1, label_name2)({__name__=~".*"}))

Limit labels that generate unnecessary combinations and aggregate metrics at a higher level:

sum(rate(http_requests_total[5m])) by (method)

2. Optimize PromQL Queries

Replace regex matches with exact matches whenever possible. For example, replace:

http_requests_total{status=~"2.*"}

with:

http_requests_total{status="200"}

Leverage functions like rate() and irate() to reduce query complexity.

3. Enable Query Caching

Use a caching layer like Thanos or Cortex, which extends Prometheus with horizontal scalability and query caching capabilities.

4. Distribute Scrape Targets

Split scrape targets across multiple Prometheus instances to balance load. Use remote write integrations to centralize data into a single backend for queries.

5. Reduce Retention Period

Lower the retention period for metrics that do not need long-term storage. Update the Prometheus configuration:

--storage.tsdb.retention.time=30d

Conclusion

High cardinality metrics and inefficient queries are common challenges in Prometheus deployments. By reducing metric cardinality, optimizing queries, and leveraging caching and load distribution strategies, teams can ensure Prometheus performs efficiently even in large-scale environments.

FAQ

Q1: How does metric cardinality impact Prometheus? A1: High cardinality metrics increase the number of time series stored and queried, leading to higher memory usage and slower query performance.

Q2: What is the best way to optimize PromQL queries? A2: Use functions like rate(), avoid regex when possible, and aggregate metrics using labels to reduce query complexity.

Q3: Can Prometheus handle horizontal scaling? A3: Prometheus itself is not horizontally scalable, but tools like Thanos and Cortex provide horizontal scalability and query federation.

Q4: How can I monitor Prometheus query performance? A4: Use the /metrics endpoint, query logs, and tools like Grafana Query Inspector to analyze query durations and patterns.

Q5: When should I use remote write integrations? A5: Use remote write integrations to centralize data from multiple Prometheus instances into a single backend for querying and long-term storage.